Basic Commands

  • Functions perform operations: funcname(input1, input2)
  • Create a vector of numbers:
x <- c(1,3,2,5)
x
## [1] 1 3 2 5
  • Open new help file window: ?funcname
  • Add two sets of numbers together:
x <- c(1,6,2)
y <- c(1,4,3)
x
## [1] 1 6 2
y
## [1] 1 4 3
length(x)
## [1] 3
length(y)
## [1] 3
x + y
## [1]  2 10  5
  • Look at a list of all objects: ls()
  • Delete any that we don’t want: rm()
  • Remove all objects at once: rm(list=ls())
  • Matrices:
x=matrix(data=c(1,2,3,4), nrow=2,ncol=2)
x
##      [,1] [,2]
## [1,]    1    3
## [2,]    2    4
x=matrix(c(1,2,3,4),2,2)
x
##      [,1] [,2]
## [1,]    1    3
## [2,]    2    4
matrix(c(1,2,3,4),2,2,byrow=TRUE)
##      [,1] [,2]
## [1,]    1    2
## [2,]    3    4
sqrt(x)
##          [,1]     [,2]
## [1,] 1.000000 1.732051
## [2,] 1.414214 2.000000
x^2
##      [,1] [,2]
## [1,]    1    9
## [2,]    4   16
  • r(norm) generates a vector of random normal variables, with first argument n the sample size:
x=rnorm(50)
y=x+rnorm(50,mean=50,sd=.1)
cor(x,y)
## [1] 0.9950526
  • set.seed() allows our code to reproduce exact same set of random numbers
  • Used whenever calculations involve random quanities
  • mean() and var() (sqrt(var())=standard deviation):
set.seed(3)
y=rnorm(100)
mean(y)
## [1] 0.01103557
var(y)
## [1] 0.7328675
sqrt(var(y))
## [1] 0.8560768
sd(y)
## [1] 0.8560768

Graphics

  • Primary way to plot data: plot()
  • Scatterplot of the numbers in x versus the numbers in y: plot(x,y)
  • Additional options, e.g. xlab applies label on the x-axis
x=rnorm(100)
y=rnorm(100)
plot(x,y)

plot(x,y,xlab="this is the x-axis",ylab="this is the y-axis",main="Plot of X vs Y")

  • Save output of an R plot: pdf(), jpeg(), etc
  • Create a sequence of numbers: seq(a,b)
x=seq(1,10)
x
##  [1]  1  2  3  4  5  6  7  8  9 10
x=1:10
x
##  [1]  1  2  3  4  5  6  7  8  9 10
x=seq(-pi,pi,length=50)
  • Represent three-dimensional data: Contour plot
  • Takes three arguments:
    1. A vector of the x values (first dimension)
    2. A vector of the y values (second dimension)
    3. A matric whose elements correspond to the z value (third dimension) for each pair of (x,y) coordinates
y=x
f=outer(x,y,function(x,y)cos(y)/(1+x^2))
contour(x,y,f)
contour(x,y,f,nlevels=45,add=T)

fa=(f-t(f))/2
contour(x,y,fa,nlevels=15)

  • image(): Works the same as contour(), except it produces a color-coded plot whose colors depend on the z value (AKA a heatmap)
  • persp(): Produce a three-dimensional plot
  • theta and phi control the angels at which the plot is viewed
image(x,y,fa)

persp(x,y,fa)

persp(x,y,fa,theta=30)

persp(x,y,fa,theta=30,phi=20)

persp(x,y,fa,theta=30,phi=70)

persp(x,y,fa,theta=30,phi=40)

Indexing Data

A=matrix(1:16,4,4)
A
##      [,1] [,2] [,3] [,4]
## [1,]    1    5    9   13
## [2,]    2    6   10   14
## [3,]    3    7   11   15
## [4,]    4    8   12   16
A[2,3]
## [1] 10
A[c(1,3),c(2,4)]
##      [,1] [,2]
## [1,]    5   13
## [2,]    7   15
A[1:3,2:4]
##      [,1] [,2] [,3]
## [1,]    5    9   13
## [2,]    6   10   14
## [3,]    7   11   15
A[1:2,]
##      [,1] [,2] [,3] [,4]
## [1,]    1    5    9   13
## [2,]    2    6   10   14
A[,1:2]
##      [,1] [,2]
## [1,]    1    5
## [2,]    2    6
## [3,]    3    7
## [4,]    4    8
A[1,]
## [1]  1  5  9 13
A[-c(1,3),]
##      [,1] [,2] [,3] [,4]
## [1,]    2    6   10   14
## [2,]    4    8   12   16
A[-c(1,3),-c(1,3,4)]
## [1] 6 8
dim(A) # number of rows followed by the number of columns given a matrix
## [1] 4 4

Loading Data

  • read.table() imports a data set
  • write.table() exports data
Auto=read.table("Auto.data.txt",header=T,na.strings="?")
#fix(Auto)
  • read.csv
Auto=na.omit(Auto)
dim(Auto)
## [1] 392   9
names(Auto)
## [1] "mpg"          "cylinders"    "displacement" "horsepower"  
## [5] "weight"       "acceleration" "year"         "origin"      
## [9] "name"

Additional Graphical and Numerical Summaries

plot(Auto$cylinders, Auto$mpg)

attach(Auto)
plot(cylinders, mpg)

cylinders=as.factor(cylinders)
plot(cylinders, mpg)

plot(cylinders, mpg, col="red")

plot(cylinders, mpg, col="red", varwidth=T)

plot(cylinders, mpg, col="red", varwidth=T,horizontal=T)

plot(cylinders, mpg, col="red", varwidth=T, xlab="cylinders", ylab="MPG")

hist(mpg)

hist(mpg,col=2)

hist(mpg,col=2,breaks=15)

pairs(Auto)

pairs(~ mpg + displacement + horsepower + weight+ acceleration, Auto)

plot(horsepower,mpg)
identify(horsepower,mpg,name)

## integer(0)
summary(Auto)
##       mpg          cylinders      displacement     horsepower   
##  Min.   : 9.00   Min.   :3.000   Min.   : 68.0   Min.   : 46.0  
##  1st Qu.:17.00   1st Qu.:4.000   1st Qu.:105.0   1st Qu.: 75.0  
##  Median :22.75   Median :4.000   Median :151.0   Median : 93.5  
##  Mean   :23.45   Mean   :5.472   Mean   :194.4   Mean   :104.5  
##  3rd Qu.:29.00   3rd Qu.:8.000   3rd Qu.:275.8   3rd Qu.:126.0  
##  Max.   :46.60   Max.   :8.000   Max.   :455.0   Max.   :230.0  
##                                                                 
##      weight      acceleration        year           origin     
##  Min.   :1613   Min.   : 8.00   Min.   :70.00   Min.   :1.000  
##  1st Qu.:2225   1st Qu.:13.78   1st Qu.:73.00   1st Qu.:1.000  
##  Median :2804   Median :15.50   Median :76.00   Median :1.000  
##  Mean   :2978   Mean   :15.54   Mean   :75.98   Mean   :1.577  
##  3rd Qu.:3615   3rd Qu.:17.02   3rd Qu.:79.00   3rd Qu.:2.000  
##  Max.   :5140   Max.   :24.80   Max.   :82.00   Max.   :3.000  
##                                                                
##                  name    
##  amc matador       :  5  
##  ford pinto        :  5  
##  toyota corolla    :  5  
##  amc gremlin       :  4  
##  amc hornet        :  4  
##  chevrolet chevette:  4  
##  (Other)           :365
summary(mpg)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    9.00   17.00   22.75   23.45   29.00   46.60